**************************************************************************************************************************************************
***Analysis of data for  [Authors]: "Voter Reactions to Candidate Background ... "****************************************************************
**************************************************************************************************************************************************
***Written for STATA 15 **************************************************************************************************************************
**************************************************************************************************************************************************


**********************************
***Installing required packages***
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*   ssc install estout //Run this line if estout is not already installed (used when creating regression tables)
*   ssc install coefplot //Run this line if "Coefplot" is not already installed (used when plotting results in graphs)
*   ssc install blindschemes //Run this line if "blindschemes is not already installed (used to control graph layout) 


*******************************
***Setting the figure scheme***
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set scheme plottig
graph set window fontface garamond

**********************
***Getting the data***
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clear all
set more off, permanently

cd "C:\Voter_reactions"
import excel "C:\Voter_reactions\rawdata_voter_reactions.xlsx", sheet("Completed and partly completed") firstrow

	
*************************
***Setting up the data***
*************************

***Pre-treatment variables***

	*Gender*
	recode PB_Gender (2=1 "female") (1=0 "male"), gen("female")

	*Age*
	gen age=PB_Age
	recode age (18/34=1 "18-34")(35/49=2 "35-49") (50/64=3 "50-64") (65/99=4 "65+"), gen(age_groups)
	label variable age Age

	*Geographical regions*
	recode PB_Region (1=1 "Hovedstaden") (2=2 "Sjælland") (3=3 "Syddanmark") (4=4 "Midtjylland") (5=5 "Nordjylland"), gen(region)

	*Education*
	recode PB_Higher_Education 	(1=1 "Grundskole") ///
								(2=2 "Studentereksamen/HF") ///
								(3=3 "HH/HTX/HHX") ///
								(4=4 "Erhvervsfaglig uddannelse") ///
								(5=5 "Kort videregående") ///
								(6=6 "Mellemlang videregående") ///
								(7=7 "Lang videregående") ///
								(8=8 "Forskeruddannelse") ///
								(9 10=9 "Andet/Ved ikke") ///
								, gen(education9)
			recode education9 	(1 2 3 4 9 10=0 "No tert") ///
								(5 6 7 8=1 "Tertiary-level education") ///
								, gen(college)
								label variable college "Tertiary-level education"
								
	*Party Choice*
	recode Q1	(1=1  "A: Socialdemokraterne") ///
				(2=2  "B: Det Radikale Venstre") ///
				(3=3  "C: Det Konservative Folkeparti") ///
				(4=4  "D: Nye Borgerlige") ///
				(5=5  "F: Socialistisk Folkeparti") ///
				(6=6  "I: Liberal Alliance") ///
				(7=7  "K: Kristendemokraterne") ///
				(8=8  "O: Dansk Folkeparti") ///
				(9=9  "V: Venstre") /// 
				(10=10 "Ø: Enhedslisten") ///
				(11=11 "Å: Alternativet") ///
				(12 13 14 15=12 "Andet/Ved ikke") ///
				, gen(partychoice)
				
			recode partychoice (1 2 5 10 11=1 "Red block") (3 4 6 7 8 9=0 "Blue block") (12=.), gen(redblock)	
				
				
	*Left-Right*
	recode Q2 (11=.), gen(unstandardized_LR)
	gen LR=unstandardized_LR/10	
	label variable LR "Left-Right Position"
	egen float right_leaning = cut(LR), group(2)

		
	*Combining LR and Party choice
	by partychoice, sort: egen partyplace_median=median(LR) 
	by partychoice, sort: egen partyplace_mean  =mean(LR)   // er mean bedre her? 
	replace partyplace_median=. if LR!=. & partychoice==12
	replace partyplace_mean  =. if LR!=. & partychoice==12
	alpha partyplace_median LR, item gen(LR_with_party_median) 							
	alpha partyplace_mean   LR, item gen(LR_with_party_mean)							
		
***Experimental conditions/treatments***

	*Female candidate*
	gen candidate_female=.
	recode candidate_female (.=0) if D_Q_Select<28
	recode candidate_female (.=1) if D_Q_Select>27
	label define gender 0 "Male" 1 "Female"
	label values candidate_female gender

	*Parents of candidate
	gen candidate_parents=.
	recode candidate_parents (.=0) if 	D_Q_Select>=1 & D_Q_Select<10	///
										| D_Q_Select>=28 & D_Q_Select<37
	recode candidate_parents (.=1) if 	D_Q_Select>=10 & D_Q_Select<19 ///
										| D_Q_Select>=37 & D_Q_Select<46
	recode candidate_parents (.=2) if 	D_Q_Select>=19 & D_Q_Select<28 ///
										| D_Q_Select>=46 & D_Q_Select<55
	label define parents  0 "No info" 1 "Factory worker" 2 "Doctors"
	label values candidate_parents parents	

	*Job of candidate*
	gen candidate_job=.
	recode candidate_job (.=0) if 			D_Q_Select==1 | D_Q_Select==2 | D_Q_Select==3 | D_Q_Select==10 | D_Q_Select==11 | D_Q_Select==12 ///
											| D_Q_Select==19 | D_Q_Select==20 | D_Q_Select==21 | D_Q_Select==28 | D_Q_Select==29 | D_Q_Select==30 ///
											| D_Q_Select==37 | D_Q_Select==38 | D_Q_Select==39 | D_Q_Select==46 | D_Q_Select==47 | D_Q_Select==48 
	recode candidate_job (.=1) if			D_Q_Select==4 | D_Q_Select==5 | D_Q_Select==6 | D_Q_Select==13 | D_Q_Select==14 | D_Q_Select==15 ///
											| D_Q_Select==22 | D_Q_Select==23 | D_Q_Select==24 | D_Q_Select==31 | D_Q_Select==32 | D_Q_Select==33 ///
											| D_Q_Select==40 | D_Q_Select==41 | D_Q_Select==42 | D_Q_Select==49 | D_Q_Select==50 | D_Q_Select==51
	recode candidate_job (.=2) if			D_Q_Select==7 | D_Q_Select==8 | D_Q_Select==9 | D_Q_Select==16 | D_Q_Select==17 | D_Q_Select==18 ///
											| D_Q_Select==25 | D_Q_Select==26 | D_Q_Select==27 | D_Q_Select==34 | D_Q_Select==35 | D_Q_Select==36 ///
											| D_Q_Select==43 | D_Q_Select==44 | D_Q_Select==45 | D_Q_Select==52 | D_Q_Select==53 | D_Q_Select==54
	label define job 0 "No info" 1 "Assistant" 2 "Lawyer"
	label values candidate_job job							

	*Policy of candidate*
	gen candidate_policy=.
	recode candidate_policy (.=0) if 	D_Q_Select==1 | D_Q_Select==4 | D_Q_Select==7 | D_Q_Select==10 | D_Q_Select==13 | D_Q_Select==16 ///
										| D_Q_Select==19 | D_Q_Select==22 | D_Q_Select==25 | D_Q_Select==28 | D_Q_Select==31 | D_Q_Select==34 ///
										| D_Q_Select==37 | D_Q_Select==40 | D_Q_Select==43 | D_Q_Select==46 | D_Q_Select==49 | D_Q_Select==52 
	recode candidate_policy (.=1) if	D_Q_Select==2 | D_Q_Select==5 | D_Q_Select==8 | D_Q_Select==11 | D_Q_Select==14 | D_Q_Select==17 ///
										| D_Q_Select==20 | D_Q_Select==23 | D_Q_Select==26 | D_Q_Select==29 | D_Q_Select==32 | D_Q_Select==35 ///
										| D_Q_Select==38 | D_Q_Select==41 | D_Q_Select==44 | D_Q_Select==47 | D_Q_Select==50 | D_Q_Select==53
	recode candidate_policy (.=2) if	D_Q_Select==3 | D_Q_Select==6 | D_Q_Select==9 | D_Q_Select==12 | D_Q_Select==15 | D_Q_Select==18 ///
										| D_Q_Select==21 | D_Q_Select==24 | D_Q_Select==27 | D_Q_Select==30 | D_Q_Select==33 | D_Q_Select==36 ///
										| D_Q_Select==39 | D_Q_Select==42 | D_Q_Select==45 | D_Q_Select==48 | D_Q_Select==51 | D_Q_Select==54
	label define policy 0 "No info" 1 "Leftwing" 2 "Rightwing"
	label values candidate_policy policy							

***Do covariates predict treatment?
	*No, they do not:
	quietly mlogit candidate_female  LR i.partychoice partyplace_median i.education9 age i.age_groups female
	di e(p)
	quietly mlogit candidate_parents LR i.partychoice partyplace_median i.education9 age i.age_groups female
	di e(p)
	quietly mlogit candidate_job     LR i.partychoice partyplace_median i.education9 age i.age_groups female
	di e(p)
	quietly mlogit candidate_policy  LR i.partychoice partyplace_median i.education9 age i.age_groups female
	di e(p)
	*quietly mlogit D_Q_Select       LR i.partychoice partyplace_median i.education9 age i.age_groups female //This one takes a long time to run (and require large matsize)
	*di e(p)
							
***Post-treatment variables***

	*Competence and Warmth*
	recode Q3_1_SQ_1 Q3_1_SQ_2 Q3_1_SQ_3 Q3_1_SQ_4 Q3_2_SQ_1 Q3_2_SQ_2 Q3_2_SQ_3 Q3_2_SQ_4 (6=.)
	gen intelligent		=Q3_1_SQ_1 
	gen competent  		=Q3_1_SQ_2 
	gen	credible		=Q3_1_SQ_3 
	gen knowledgeable	=Q3_1_SQ_4 
	gen likeable		=Q3_2_SQ_1 
	gen conscientious	=Q3_2_SQ_2 
	gen friendly		=Q3_2_SQ_3
	gen caring			=Q3_2_SQ_4

	factor intelligent competent credible knowledgeable likeable conscientious friendly caring 
	rotate //the eight trait items load on two factors (after rotation)

	alpha competent intelligent credible knowledgeable	, std item gen(unstandardised_competence) //highly reliable scale
	alpha likeable conscientious friendly caring		, std item gen(unstandardised_warmth)	//highly reliable scale

	egen unstandardised_competence_min=min(unstandardised_competence) //the lines standardize the measure to 0-1
	egen unstandardised_competence_max=max(unstandardised_competence)
	gen competence=(unstandardised_competence-unstandardised_competence_min)/(unstandardised_competence_max-unstandardised_competence_min)
	drop unstandardised_competence unstandardised_competence_max unstandardised_competence_min

	egen unstandardised_warmth_min=min(unstandardised_warmth) //the lines standardize the measure to 0-1
	egen unstandardised_warmth_max=max(unstandardised_warmth)
	gen warmth=(unstandardised_warmth-unstandardised_warmth_min)/(unstandardised_warmth_max-unstandardised_warmth_min)
	drop unstandardised_warmth unstandardised_warmth_max unstandardised_warmth_min

	*Perception of candidate Left-Right position*
	recode Q4 (11=.), gen(unstandardised_candidate_LR)
	gen candidate_LR=unstandardised_candidate_LR/10

	*Likelihood of Voting for Candidate
	recode Q5 (11=.), gen(unstandardised_voting_candidate)
	gen voting_for_candidate=unstandardised_voting_candidate/10

	***dropping obsolete variables
	drop 	PB_Gender PB_Age PB_Region PB_Higher_Education Q1 partyplace_median Q3_1_SQ_1 Q3_1_SQ_2 Q3_1_SQ_3 Q3_1_SQ_4 Q3_2_SQ_1 Q3_2_SQ_2 Q3_2_SQ_3 Q3_2_SQ_4 Q4 Q5 ///
			unstandardised_candidate_LR unstandardised_voting_candidate
	
	
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***ANALYSES****************************************************************************************************************************************************************************
***************************************************************************************************************************************************************************************

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***Respondent Descriptives**********
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tab female if Completed==1
sum age if Completed==1
tab college if Completed==1
sum LR if Completed==1
tab redblock if Completed==1
tab partychoice if partychoice<12 & Completed==1 

global resp_controls i.female age i.college LR

tab Completed //92.4 percent completion rate
tab Completed if D_Q_Select!=. //Drop out after potential exposure to stimuli just 5.3%

*Attrition across conditions - there are no significant differences
tab Completed D_Q_Select if D_Q_Select!=., chi // note, does not have exp count > 5 for all cells
tab Completed candidate_female    if D_Q_Select!=., chi
tab Completed candidate_parents   if D_Q_Select!=., chi
tab Completed candidate_job		  if D_Q_Select!=., chi
tab Completed candidate_policy    if D_Q_Select!=., chi

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***Models***************************
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***Distribution on DVs***

*twoway (kdensity competence) (kdensity warmth) (kdensity candidate_LR) (kdensity voting_for_candidate), title("Distribution on DVs") xtitle("")
*graph export distribution_on_DVs.tif, replace
sum competence warmth candidate_LR voting_for_candidate


***REGRESSIONS***

***MODELS USED IN PAPER - MODELS INCLUDE INTERACTIONS BETWEEN CANDIDATE POLICY POSITION AND OTHER TREATMENTS***

	*Competence
	reg competence i.candidate_female##i.candidate_policy i.candidate_parents##i.candidate_policy i.candidate_job##i.candidate_policy 
	eststo model_int_policy_competence 
	margins //  .5638762 
	margins, at(candidate_female=(0 1) candidate_policy=0) 
	margins, at(candidate_female=(0 1) candidate_policy=0) pwcompare(effect) //no significant effect of gender 
	margins, at(candidate_female=(0 1) candidate_policy=1) 
	margins, at(candidate_female=(0 1) candidate_policy=1) pwcompare(effect) //no significant effect of gender 
	margins, at(candidate_female=(0 1) candidate_policy=2) 
	margins, dydx(candidate_female) at(candidate_policy=(0 1 2)) 
	margins, at(candidate_female=(0 1) candidate_policy=2) pwcompare(effect) //no significant effect of gender 
	margins, at(candidate_parents=(0 1 2) candidate_policy=0) 
	margins, at(candidate_parents=(0 1 2) candidate_policy=0) pwcompare(effect)  //no significant effect of parents 
	margins, at(candidate_parents=(0 1 2) candidate_policy=1) 
	margins, at(candidate_parents=(0 1 2) candidate_policy=1) pwcompare(effect)  //no significant effect of parents 
	margins, at(candidate_parents=(0 1 2) candidate_policy=2) 
	margins, at(candidate_parents=(0 1 2) candidate_policy=2) pwcompare(effect)  //no significant effect of parents 
	margins, at(candidate_job=(0 1 2) candidate_policy=0) 
	margins, at(candidate_job=(0 1 2) candidate_policy=0) pwcompare(effect)  //lawyer (marginally) significantly more competent than other groups
	margins, at(candidate_job=(0 1 2) candidate_policy=1) 
	margins, at(candidate_job=(0 1 2) candidate_policy=1) pwcompare(effect)  //lawyer more competent than other groups
	margins, at(candidate_job=(0 1 2) candidate_policy=2) 
	margins, at(candidate_job=(0 1 2) candidate_policy=2) pwcompare(effect)  //lawyer much more competent than other groups and assistant marginally less competent than no info
	
	*Warmth
	reg warmth i.candidate_female##i.candidate_policy i.candidate_parents##i.candidate_policy i.candidate_job##i.candidate_policy 
	eststo model_int_policy_warmth 
	margins //  .6349146 
	margins, at(candidate_female=(0 1) candidate_policy=0) 
	margins, at(candidate_female=(0 1) candidate_policy=0) pwcompare(effect) //no significant effect of gender 
	margins, at(candidate_female=(0 1) candidate_policy=1) 
	margins, at(candidate_female=(0 1) candidate_policy=1) pwcompare(effect) //no significant effect of gender 
	margins, at(candidate_female=(0 1) candidate_policy=2) 
	margins, at(candidate_female=(0 1) candidate_policy=2) pwcompare(effect) //no significant effect of gender 
	margins, at(candidate_parents=(0 1 2) candidate_policy=0) 
	margins, at(candidate_parents=(0 1 2) candidate_policy=0) pwcompare(effect)   
	margins, at(candidate_parents=(0 1 2) candidate_policy=1) 
	margins, at(candidate_parents=(0 1 2) candidate_policy=1) pwcompare(effect)  
	margins, at(candidate_parents=(0 1 2) candidate_policy=2) 
	margins, at(candidate_parents=(0 1 2) candidate_policy=2) pwcompare(effect)  
	margins, at(candidate_parents=(0 1 2) candidate_policy=(0 1 2)) post coefleg
	lincom   _b[2._at]- _b[1bn._at] 
	lincom   _b[5._at]- _b[4._at]
	lincom   _b[8._at]- _b[7._at]
	lincom  (_b[2._at]- _b[1bn._at])-(_b[5._at]- _b[4._at]) // being a worker vs. non-info is a significantly larger advantage when policy is not known as opposed to left-leaning
	lincom (_b[2._at]- _b[3._at])-(_b[5._at]- _b[6._at]) 	// being a worker vs. doctor
	lincom  (_b[2._at]- _b[1bn._at])-(_b[8._at]- _b[7._at]) // being a worker vs. non-info is not significantly larger advantage when policy is not known as opposed to right-leaning	
	lincom  (_b[2._at]- _b[3._at])-(_b[8._at]- _b[9._at]) // being a worker vs. doctor
	estimates restore model_int_policy_warmth
	margins, at(candidate_job=(0 1 2) candidate_policy=0) 
	margins, at(candidate_job=(0 1 2) candidate_policy=0) pwcompare(effect)  
	margins, at(candidate_job=(0 1 2) candidate_policy=1) 
	margins, at(candidate_job=(0 1 2) candidate_policy=1) pwcompare(effect)  
	margins, at(candidate_job=(0 1 2) candidate_policy=2) 
	margins, at(candidate_job=(0 1 2) candidate_policy=2) pwcompare(effect)  
	
	*Left-Right position
	reg candidate_LR i.candidate_female##i.candidate_policy i.candidate_parents##i.candidate_policy i.candidate_job##i.candidate_policy 
	eststo model_int_policy_LR 
	margins //  .4883796 
	margins, at(candidate_female=(0 1) candidate_policy=0) 
	margins, at(candidate_female=(0 1) candidate_policy=0) pwcompare(effect) //no significant effect of gender 
	margins, at(candidate_female=(0 1) candidate_policy=1) 
	margins, at(candidate_female=(0 1) candidate_policy=1) pwcompare(effect) //no significant effect of gender 
	margins, at(candidate_female=(0 1) candidate_policy=2) 
	margins, at(candidate_female=(0 1) candidate_policy=2) pwcompare(effect) //no significant effect of gender 
	margins, at(candidate_parents=(0 1 2) candidate_policy=0) 
	margins, at(candidate_parents=(0 1 2) candidate_policy=0) pwcompare(effect)  //people infer policy position from parents' occupation
	margins, at(candidate_parents=(0 1 2) candidate_policy=1) 
	margins, at(candidate_parents=(0 1 2) candidate_policy=1) pwcompare(effect)  //when the policy is leftwing, the effect of parents' occ is about one third of when no policy cue
	margins, at(candidate_parents=(0 1 2) candidate_policy=2) 
	margins, at(candidate_parents=(0 1 2) candidate_policy=2) pwcompare(effect)  //when the policy is rightwing, the effect of parents' occ is about one third of when no policy cue
	margins, at(candidate_parents=(0 1 2) candidate_policy=(0 1 2)) post coefleg
	lincom  (_b[2._at]- _b[1bn._at])-(_b[5._at]- _b[4._at]) // being a worker vs. non-info when policy is not known as opposed to left-leaning
	lincom (_b[2._at]- _b[3._at])-(_b[5._at]- _b[6._at]) 	// being a worker vs. doctor
	lincom  (_b[2._at]- _b[1bn._at])-(_b[8._at]- _b[7._at]) // being a worker vs. non-info when policy is not known as opposed to right-leaning	
	lincom  (_b[2._at]- _b[3._at])-(_b[8._at]- _b[9._at]) // being a worker vs. doctor
	lincom  (_b[3._at]- _b[6._at])-(_b[2._at]- _b[5._at]) // the difference in LR between doctors and worker is significantly smalle when left-wing position is known (relative to no policy info)
	lincom  (_b[3._at]- _b[9._at])-(_b[2._at]- _b[8._at]) // the difference in LR between doctors and worker is significantly smalle when left-wing position is known (relative to no policy info)
	estimates restore model_int_policy_LR 
	margins, at(candidate_job=(0 1 2) candidate_policy=0) 
	margins, at(candidate_job=(0 1 2) candidate_policy=0) pwcompare(effect) 
	margins, at(candidate_job=(0 1 2) candidate_policy=1) 
	margins, at(candidate_job=(0 1 2) candidate_policy=1) pwcompare(effect)  
	margins, at(candidate_job=(0 1 2) candidate_policy=2) 
	margins, at(candidate_job=(0 1 2) candidate_policy=2) pwcompare(effect)  
	
	*Likelihood of vote
	reg voting_for_candidate i.candidate_female##i.candidate_policy i.candidate_parents##i.candidate_policy i.candidate_job##i.candidate_policy 
	eststo model_int_policy_vote 
	margins //  .30449
	margins, at(candidate_policy=(0 1 2)) 
	margins, at(candidate_female=(0 1) candidate_policy=0) 
	margins, at(candidate_female=(0 1) candidate_policy=0) pwcompare(effect) //no significant effect of gender 
	margins, at(candidate_female=(0 1) candidate_policy=1) 
	margins, at(candidate_female=(0 1) candidate_policy=1) pwcompare(effect) //no significant effect of gender 
	margins, at(candidate_female=(0 1) candidate_policy=2) 
	margins, at(candidate_female=(0 1) candidate_policy=2) pwcompare(effect) //no significant effect of gender 
	margins, at(candidate_parents=(0 1 2) candidate_policy=0) 
	margins, at(candidate_parents=(0 1 2) candidate_policy=0) pwcompare(effect)  //no significant effect of parents 
	margins, at(candidate_parents=(0 1 2) candidate_policy=1) 
	margins, at(candidate_parents=(0 1 2) candidate_policy=1) pwcompare(effect)  //no significant effect of parents 
	margins, at(candidate_parents=(0 1 2) candidate_policy=2) 
	margins, at(candidate_parents=(0 1 2) candidate_policy=2) pwcompare(effect)  //no significant effect of parents 
	margins, at(candidate_parents=(0 1 2) candidate_policy=(0 1 2)) post coefleg
	lincom  (_b[3._at]- _b[1bn._at])-(_b[6._at]- _b[4._at]) // being a doctor vs. non-info when policy is not known as opposed to left-leaning
	lincom (_b[3._at]- _b[3._at])-(_b[5._at]- _b[6._at]) 	// being a doctor vs  non-info when policy is not known as opposed to right-leaning
	lincom  (_b[2._at]- _b[1bn._at])-(_b[5._at]- _b[4._at]) // being a worker vs. non-info when policy is not known as opposed to left-leaning
	lincom (_b[2._at]- _b[3._at])-(_b[5._at]- _b[6._at]) 	// being a worker vs. doctor
	lincom  (_b[2._at]- _b[1bn._at])-(_b[8._at]- _b[7._at]) // being a worker vs. non-info when policy is not known as opposed to right-leaning	
	lincom  (_b[2._at]- _b[3._at])-(_b[8._at]- _b[9._at]) // being a worker vs. doctor
	
	estimates restore model_int_policy_vote 
	margins, at(candidate_job=(0 1 2) candidate_policy=0) 
	margins, at(candidate_job=(0 1 2) candidate_policy=0) pwcompare(effect)  //lawyer significantly less popular than assistant
	margins, at(candidate_job=(0 1 2) candidate_policy=1) 
	margins, at(candidate_job=(0 1 2) candidate_policy=1) pwcompare(effect)  //lawyer significantly more popular than assistant
	margins, at(candidate_job=(0 1 2) candidate_policy=2) 
	margins, at(candidate_job=(0 1 2) candidate_policy=2) pwcompare(effect)  //no significant effect

*ALTERNATIVE MODELS - INCLUDED IN APPENDIX:

	*Models withouth any interactions - as described in pre-analysis plan:
	
		*Competence
		reg competence i.candidate_female i.candidate_parents i.candidate_job i.candidate_policy 
		eststo model_competence 
		margins //  .5638762 
		margins, at(candidate_female=(0 1)) 
		margins, at(candidate_female=(0 1)) pwcompare(effect) //no significant effect of gender 
		margins, at(candidate_parents=(0 1 2)) 
		margins, at(candidate_parents=(0 1 2)) pwcompare(effect)  //no significant effect of parents 
		margins, at(candidate_job=(0 1 2)) 
		margins, at(candidate_job=(0 1 2)) pwcompare(effect)  //lawyer significantly more competent than other groups
		di .0532912 / .1852022  // difference between lawyer and asssistant is .29 SD
		di .0518362 / .1852022  // difference between lawyer and no info is .28 SD
		margins, at(candidate_policy=(0 1 2)) 
		margins, at(candidate_policy=(0 1 2)) pwcompare(effect)  //significant differences: leftwing > no info > rightwing

		*Warmth
		reg warmth i.candidate_female i.candidate_parents i.candidate_job i.candidate_policy 
		eststo model_warmth
		margins // .6349146 
		margins, at(candidate_female=(0 1)) 
		margins, at(candidate_female=(0 1)) pwcompare(effect) //no significant effect of gender 
		margins, at(candidate_parents=(0 1 2)) 
		margins, at(candidate_parents=(0 1 2)) pwcompare(effect)  //factory worker significantly warmer than other groups
		di -.0343675 /.1901623 //difference between factory worker and doctor is .18 SD
		margins, at(candidate_job=(0 1 2)) 
		margins, at(candidate_job=(0 1 2)) pwcompare(effect)  //assistant marginally significantly warmer than lawyer
		margins, at(candidate_policy=(0 1 2)) 
		margins, at(candidate_policy=(0 1 2)) pwcompare(effect)  //significant differences: leftwing > no info > rightwing

		*Left-Right position***
		reg candidate_LR i.candidate_female i.candidate_parents i.candidate_job i.candidate_policy 
		eststo model_LR
		margins //  .4883796 
		margins, at(candidate_female=(0 1)) 
		margins, at(candidate_female=(0 1)) pwcompare(effect) //no significant effect of gender 
		margins, at(candidate_parents=(0 1 2)) 
		margins, at(candidate_parents=(0 1 2)) pwcompare(effect)  //significant differences: doctor > no info > factory workers
		di  .0768156  / .2725881 //difference between factory worker and doctor is .28 SD
		margins, at(candidate_job=(0 1 2)) 
		margins, at(candidate_job=(0 1 2)) pwcompare(effect)  //significant differences: lawyer > no info > assistant
		di .051457 /   .259991
		margins, at(candidate_policy=(0 1 2)) 
		margins, at(candidate_policy=(0 1 2)) pwcompare(effect)  //obviously, large differences rightwing > no info > leftwing 


		*Likelihood of vote
		reg voting_for_candidate i.candidate_female i.candidate_parents i.candidate_job i.candidate_policy 
		eststo model_vote
		margins //  .4883796 
		margins, at(candidate_female=(0 1)) 
		margins, at(candidate_female=(0 1)) pwcompare(effect) //no significant effect of gender 
		margins, at(candidate_parents=(0 1 2)) 
		margins, at(candidate_parents=(0 1 2)) pwcompare(effect)  //no significant effect of parents 
		margins, at(candidate_job=(0 1 2)) 
		margins, at(candidate_job=(0 1 2)) pwcompare(effect)  //no significant effect of job
		margins, at(candidate_policy=(0 1 2)) 
		margins, at(candidate_policy=(0 1 2)) pwcompare(effect)  //significant differences:  leftwing > no info > rightwing
		di  -.1248657 / .2749633 //difference between left and rightwing is .45 SD 


	*Models with respondent covariates	
	
		*Competence
		reg competence i.candidate_female i.candidate_parents i.candidate_job i.candidate_policy $resp_controls
		eststo model_competence_contr 
		margins, at(candidate_female=(0 1)) pwcompare(effect) //no significant effect of gender 
		margins, at(candidate_parents=(0 1 2)) pwcompare(effect)  //no significant effect of parents 
		margins, at(candidate_job=(0 1 2)) pwcompare(effect)  //lawyer significantly more competent than other groups
		margins, at(candidate_policy=(0 1 2)) pwcompare(effect)  //significant differences: leftwing > no info > rightwing
			
		*Warmth
		reg warmth i.candidate_female i.candidate_parents i.candidate_job i.candidate_policy $resp_controls
		eststo model_warmth_contr
		margins, at(candidate_female=(0 1)) pwcompare(effect) //no significant effect of gender 
		margins, at(candidate_parents=(0 1 2)) pwcompare(effect)  //factory worker significantly warmer than other groups
		margins, at(candidate_job=(0 1 2)) pwcompare(effect)  //lawyer significantly more competent than other groups
		margins, at(candidate_policy=(0 1 2)) pwcompare(effect)  //significant differences: leftwing > no info > rightwing
			
		*Left-Right position
		reg candidate_LR i.candidate_female i.candidate_parents i.candidate_job i.candidate_policy $resp_controls
		eststo model_LR_contr
		margins, at(candidate_female=(0 1)) pwcompare(effect) //no significant effect of gender 
		margins, at(candidate_parents=(0 1 2)) pwcompare(effect)  // significant differences: lawyer > no info > assistant
		margins, at(candidate_job=(0 1 2)) pwcompare(effect)  //significant differences: doctor > no info > factory workers
		margins, at(candidate_policy=(0 1 2)) pwcompare(effect)  //significant differences: lawyer > no info > assistant

		*Likelihood of vote
		reg voting_for_candidate i.candidate_female i.candidate_parents i.candidate_job i.candidate_policy $resp_controls
		eststo model_vote_contr
		margins, at(candidate_female=(0 1)) pwcompare(effect) //no significant effect of gender 
		margins, at(candidate_parents=(0 1 2)) pwcompare(effect)  //no significant effect of parents 
		margins, at(candidate_job=(0 1 2)) pwcompare(effect)  //no significant effect of job
		margins, at(candidate_policy=(0 1 2)) pwcompare(effect)  //significant differences:  leftwing > no info > rightwing
	
	
	*Models with interactions between job of parents and job of candidate*
	
		*Competence
		reg competence i.candidate_female i.candidate_parents##i.candidate_job i.candidate_policy 
		eststo model_competence_par_job
			
		*Warmth
		reg warmth i.candidate_female i.candidate_parents##i.candidate_job i.candidate_policy 
		eststo model_warmth_par_job
			
		*Left-Right position
		reg candidate_LR i.candidate_female i.candidate_parents##i.candidate_job i.candidate_policy 
		eststo model_LR_par_job

		*Likelihood of vote
		reg voting_for_candidate i.candidate_female i.candidate_parents##i.candidate_job i.candidate_policy
		eststo model_vote_par_job
	
				
	*Models interacted on left- and right-leaning voter
		reg warmth i.candidate_female##i.candidate_policy##c.LR i.candidate_parents##i.candidate_policy##c.LR i.candidate_job##i.candidate_policy##c.LR 
		eststo  model_int_policy_warmth_LR 
		
		reg competence i.candidate_female##i.candidate_policy##c.LR i.candidate_parents##i.candidate_policy##c.LR i.candidate_job##i.candidate_policy##c.LR 
		eststo model_int_policy_comp_LR
						
		reg candidate_LR i.candidate_female##i.candidate_policy##c.LR i.candidate_parents##i.candidate_policy##c.LR i.candidate_job##i.candidate_policy##c.LR 
		eststo  model_int_policy_LR_LR
		
		reg voting_for_candidate i.candidate_female##i.candidate_policy##c.LR i.candidate_parents##i.candidate_policy##c.LR i.candidate_job##i.candidate_policy##c.LR 
		eststo  model_int_policy_vote_LR
		

**********************************************************************************************************************					
***FIGURES, controlling layouts***************************************************************************************
**********************************************************************************************************************
set scheme plotplain

*Controlling appearence of marker, CI and plotregion:
global marker_ci 	msymbol(circle) msize(medsmall) mfcolor(white) mlcolor(black) ciopts(lcolor(black..)) yscale(lcolor(none) ) xscale(lcolor(none)) xlabel(, grid) ylabel(, grid) grid(within glcolor(gs16))  ///
					level(95 83 83 83) ///
					plotregion(fcolor(gs16) lcolor(black) lwidth(vthin)) 

*Coeflabels
global coeflabels_gender 	coeflabels(	1._at=`" "                " "Male"   " "  "'   	///
										2._at=`" "                " "Female" " "  "', labsize(medium) labcolor(black) tlcolor(black) )
global coeflabels_parents 	coeflabels(	1._at=`" "                " "No info"    " " "'  ///
										2._at=`" "                " "Factory w." " " "'  ///
										3._at=`" "                " "Doctors"    " " "', labsize(medium) labcolor(black) tlcolor(black) ) 
global coeflabels_job  		coeflabels(	1._at=`" "                " "No info"  " "  "'   ///
										2._at=`" "                " "Assistant " "  "'   ///
										3._at=`" "                " "Lawyer"   " "  "' , labsize(medium) labcolor(black) tlcolor(black) ) 
global coeflabels_policy  	coeflabels(	1._at=`" "           " "No info"  " "  "'   ///
										2._at=`" "           " "Left " "  "'   ///
										3._at=`" "           " "Right"   " "  "' , labsize(medium) labcolor(black) tlcolor(black) ) 
*Means
global mean_com 	xline(.5638762, lwidth(medium) lcolor(black) lpattern(dash)) 
global mean_war 	xline(.6349146, lwidth(medium) lcolor(black) lpattern(dash))   										
global mean_LR 		xline(.4883796, lwidth(medium) lcolor(black) lpattern(dash))
global mean_vote	xline(.30449, lwidth(medium) lcolor(black) lpattern(dash))	

*(Not) bottom/left graph
global notbot 		xlabel(none) xscale(off)
global notleft		ylabel(none) yscale(off)
global bottom		xlabel(#5, nogrid labsize(medsmall) labcolor(black) tlcolor(black) )

*Ytitles
global yGender 	ytitle("Gender" " ", size(medium)) fysize(70)
global yParent 	ytitle("Parents'" "occupation", size(medium)) fysize(90)
global yJob 	ytitle("Candidates'" "occupation", size(medium)) fysize(120)
global yPolicy 	ytitle("Policy position" " ", size(medium)) fysize(120)

global nyGender ytitle("", size(medium)) fysize(70)
global nyParent ytitle("", size(medium)) fysize(90)
global nyJob 	ytitle("", size(medium)) fysize(120)
global nyPolicy ytitle("", size(medium)) fysize(120)

global NOPolicy	ytitle("No Policy", size(medium)) fysize(100)
global LPolicy 	ytitle("Left", size(medium)) fysize(100)
global RPolicy 	ytitle("Right", size(medium)) fysize(130)
global nNOPolicy	ytitle("", size(medium)) fysize(100)
global nLPolicy ytitle("", size(medium)) fysize(100)
global nRPolicy ytitle("", size(medium)) fysize(130)
										
										
******************************************					
***FIGURES, MODELS WITHOUT INTERACTIONS***					
******************************************
		
*Competence*
estimates restore model_competence
margins
margins, at(candidate_female=(0 1)) post
eststo com_fem
	coefplot, 	$marker_ci $coeflabels_gender $mean_com $notbot $yGender			
				graph save  com_fem.gph, replace 
estimates restore model_competence		
margins, at(candidate_parents=(0 1 2)) post 
eststo com_par
	coefplot, 	$marker_ci $coeflabels_parents $mean_com $notbot $yParent 			
				graph save  com_par.gph, replace 
estimates restore model_competence		
margins, at(candidate_job=(0 1 2)) post
eststo com_job
	coefplot, 	$marker_ci $coeflabels_job $mean_com $yJob $bottom 
				graph save  com_job.gph, replace 						
graph 	combine com_fem.gph com_par.gph com_job.gph, col(1) imargin(small) iscale(1) xcommon title("                          Competence") fxsize(130)
graph save 		com_main.gph, replace

*Competence - policy*
estimates restore model_competence		
margins, at(candidate_policy=(0 1 2)) post coefleg
eststo com_policy
	coefplot, 	$marker_ci $coeflabels_policy $mean_com $yPolicy  $bottom ///
				title("       Competence") fxsize(110)
				graph save  com_policy.gph, replace 
			
*Warmth
estimates restore model_warmth
margins
margins, at(candidate_female=(0 1)) post coefleg
eststo war_fem
	coefplot, 	$marker_ci $mean_war $notbot $notleft $yGender 			 
				graph save  war_fem.gph, replace 
estimates restore model_warmth		
margins, at(candidate_parents=(0 1 2)) post coefleg
eststo war_par
	coefplot, $marker_ci $mean_war $notbot $notleft	$yParent
				graph save  war_par.gph, replace 
estimates restore model_warmth		
margins, at(candidate_job=(0 1 2)) post coefleg
eststo war_job
	coefplot, $marker_ci $mean_war	$notleft $nyJob $bottom 
				graph save  war_job.gph, replace 						
graph combine war_fem.gph war_par.gph war_job.gph, col(1) imargin(small) iscale(1) xcommon  title("Warmth") fxsize(70)
graph save war_main.gph, replace

	
*Warmth - policy*
estimates restore model_warmth	
margins	
margins, at(candidate_policy=(0 1 2)) post
eststo war_policy
	coefplot, $marker_ci $mean_war $yPolicy $bottom $notleft ///
			title("Warmth") fxsize(70)
			graph save  war_policy.gph, replace 
	
*LR
estimates restore model_LR
margins
margins, at(candidate_female=(0 1)) post
eststo LR_fem
	coefplot, 	$marker_ci $mean_LR $notbot $notleft	$yGender  
				graph save  LR_fem.gph, replace 
estimates restore model_LR		
margins, at(candidate_parents=(0 1 2)) post
eststo LR_par
	coefplot, 	$marker_ci $mean_LR $notbot $notleft  $yParent
				graph save  LR_par.gph, replace 
estimates restore model_LR		
margins, at(candidate_job=(0 1 2)) post
eststo LR_job
	coefplot, 	$marker_ci $mean_LR $notleft $nyJob $bottom   
				graph save  LR_job.gph, replace 						
graph combine LR_fem.gph LR_par.gph LR_job.gph,  col(1) imargin(small) iscale(1) xcommon  title("Left-Right") fxsize(70)
graph save LR_main.gph, replace
	
*LR - policy*
estimates restore model_LR		
margins, at(candidate_policy=(0 1 2)) post
eststo LR_policy
	coefplot, 	$marker_ci $mean_LR $notleft $nyPolicy $bottom  $bottom ///
				title("Left-Right") fxsize(70)
				graph save  LR_policy.gph, replace 
			
			
*Vote
estimates restore model_vote
margins
margins, at(candidate_female=(0 1)) post
eststo vote_fem
	coefplot, 	$marker_ci $mean_vote $notbot $notleft  $nyGender
				graph save  vote_fem.gph, replace 
estimates restore model_vote		
margins, at(candidate_parents=(0 1 2)) post
eststo vote_par
	coefplot, 	$marker_ci $mean_vote  $notbot $notleft  $nyParent  
				graph save  vote_par.gph, replace 
estimates restore model_vote		
margins, at(candidate_job=(0 1 2)) post
eststo vote_job
	coefplot, 	$marker_ci $mean_vote $notleft $nyJob $bottom   
				graph save  vote_job.gph, replace
	
graph combine vote_fem.gph vote_par.gph vote_job.gph, col(1) imargin(small) iscale(1) xcommon  title("Vote") fxsize(70) 
graph save vote_main.gph, replace			
				
*Vote - policy*
estimates restore model_vote		
margins, at(candidate_policy=(0 1 2)) post
eststo vote_policy
	coefplot, 	$marker_ci $mean_vote $notleft $bottom ///
				title("Vote") fxsize(70)
				graph save  vote_policy.gph, replace 
			

*FIGURE 1 (Policy position)*FIGURE 1: Voter Evaluations of a Candidate, Conditional on Policy Information
	graph combine 	com_policy.gph war_policy.gph LR_policy.gph vote_policy.gph, imargin(small) col(4) xsize(6.5) ysize(2) iscale(1.5)  ///
					title("", size(huge) margin(bottom)) ///
					note("Note: Predicted values with 95% Confidence intervals (Thicker lines are 83% C.I.)." "Vertical dashed lines denote the mean value of variable.", size(vlarge))
					graph export FIGURE_1NEW.emf, replace			
	
*FIGURE C1 (Gender, Parent, Job)*FIGURE 1: Voter Evaluations of a Candidate, Conditional on Background Characteristics of Candidate
	graph combine 	com_main.gph war_main.gph LR_main.gph vote_main.gph , imargin(small) col(4) xsize(6.5) ysize(3.5) iscale(1) ///
					title("", size(medlarge)) ///
					note(" " "Note: Predicted values with 95% Confidence intervals (Thicker lines are 83% C.I.) Vertical dashed lines denote mean value of variable", size(medsmall))
					graph export FIGURE_C1.emf, replace

		
*************************************					
***FIGURES, WITH INTERACTIONS********					
*************************************

*Gender	
	*Competence 
	estimates restore model_int_policy_competence
	margins, at(candidate_female=(0 1) candidate_policy=(0)) post coefleg
	eststo com_fem_intpolicy0
		coefplot, 	$marker_ci $coeflabels_gender $mean_com  $notbot $NOPolicy
					graph save  int_policy_gender_competence0.gph, replace
	estimates restore model_int_policy_competence
	margins, at(candidate_female=(0 1) candidate_policy=(1)) post
	eststo com_fem_intpolicy1
		coefplot,	$marker_ci $coeflabels_gender $mean_com  $notbot $LPolicy
					graph save  int_policy_gender_competence1.gph, replace
	estimates restore model_int_policy_competence
	margins, at(candidate_female=(0 1) candidate_policy=(2)) post
	eststo com_fem_intpolicy2
		coefplot, 	$marker_ci $coeflabels_gender $mean_com $RPolicy $bottom  
					graph save  int_policy_gender_competence2.gph, replace
			
	graph combine 	int_policy_gender_competence0.gph int_policy_gender_competence1.gph  int_policy_gender_competence2.gph, imargin(small) iscale(1) title("                      Competence") xcommon col(1) fxsize(110)
	graph save 		com_int_pol_gen.gph, replace		
			
	*Warmth
	estimates restore model_int_policy_warmth
	margins, at(candidate_female=(0 1) candidate_policy=(0)) post coefleg
	eststo war_fem_intpolicy0
		coefplot, 	$marker_ci $coeflabels_gender $mean_war  $notbot $NOPolicy
					graph save  int_policy_gender_warmth0.gph, replace
	estimates restore model_int_policy_warmth
	margins, at(candidate_female=(0 1) candidate_policy=(1)) post
	eststo war_fem_intpolicy1
		coefplot, 	$marker_ci $coeflabels_gender $mean_war  $notbot $LPolicy
					graph save  int_policy_gender_warmth1.gph, replace
	estimates restore model_int_policy_warmth
	margins, at(candidate_female=(0 1) candidate_policy=(2)) post
	eststo war_fem_intpolicy2
		coefplot, 	$marker_ci $coeflabels_gender $mean_war $RPolicy $bottom 
					graph save  int_policy_gender_warmth2.gph, replace
			
	graph combine 	int_policy_gender_warmth0.gph int_policy_gender_warmth1.gph  int_policy_gender_warmth2.gph, imargin(small) iscale(1) title("                      Warmth") xcommon col(1) 	fxsize(110)		
	graph save 		war_int_pol_gen.gph, replace		
			
	*LR
	estimates restore model_int_policy_LR
	margins, at(candidate_female=(0 1) candidate_policy=(0)) post coefleg
	eststo LR_fem_intpolicy0
		coefplot, 	$marker_ci $coeflabels_gender $mean_LR  $notbot $notleft $NOPolicy 
					graph save  int_policy_gender_LR0.gph, replace
	estimates restore model_int_policy_LR
	margins, at(candidate_female=(0 1) candidate_policy=(1)) post
	eststo LR_fem_intpolicy1
		coefplot, 	$marker_ci $coeflabels_gender $mean_LR  $notbot $notleft $LPolicy 
					graph save  int_policy_gender_LR1.gph, replace
	estimates restore model_int_policy_LR
	margins, at(candidate_female=(0 1) candidate_policy=(2)) post
	eststo LR_fem_intpolicy2
		coefplot, 	$marker_ci $coeflabels_gender $mean_LR $notleft $RPolicy $bottom 
					graph save  int_policy_gender_LR2.gph, replace
			
	graph combine 	int_policy_gender_LR0.gph int_policy_gender_LR1.gph  int_policy_gender_LR2.gph, imargin(small) iscale(1) title("Left-Right") xcommon col(1) fxsize(70) 			
	graph save 		LR_int_pol_gen.gph, replace		
			
	*Vote
	estimates restore model_int_policy_vote
	margins, at(candidate_female=(0 1) candidate_policy=(0)) post coefleg
	eststo vote_fem_intpolicy0
		coefplot, 	$marker_ci $coeflabels_gender $mean_vote  $notbot $notleft $NOPolicy 
					graph save  int_policy_gender_vote0.gph, replace
	estimates restore model_int_policy_vote
	margins, at(candidate_female=(0 1) candidate_policy=(1)) post
	eststo vote_fem_intpolicy1
		coefplot, 	$marker_ci $coeflabels_gender $mean_vote  $notbot $notleft $LPolicy 
					graph save  int_policy_gender_vote1.gph, replace
	estimates restore model_int_policy_vote
	margins, at(candidate_female=(0 1) candidate_policy=(2)) post
	eststo vote_fem_intpolicy2
		coefplot, 	$marker_ci $coeflabels_gender $mean_vote $notleft  $RPolicy $bottom 
					graph save  int_policy_gender_vote2.gph, replace
			
	graph combine 		int_policy_gender_vote0.gph int_policy_gender_vote1.gph  int_policy_gender_vote2.gph, imargin(small) iscale(1) title("Vote") xcommon col(1) fxsize(70)			
	graph save vote_int_pol_gen.gph, replace		
			
			
	
	*NEW: FIGURE 2*FIGURE 2: Voter Evaluations of a Candidate, Conditional on Candidate Gender and Policy information				
	graph combine 	war_int_pol_gen.gph LR_int_pol_gen.gph vote_int_pol_gen.gph , imargin(zero) col(4) xsize(6.5) ysize(3) iscale(1)  ///
					title("", size(medlarge) margin(bottom))		///
					note("Note: Predicted values with 95% Confidence intervals (Thicker lines are 83% C.I.)." "Vertical dashed lines denote the mean value of variable.", size(medsmall))
					graph export FIGURE_2NEW.emf, replace	
	
				
					
	
*Parents' occupation
	*Competence 
	estimates restore model_int_policy_competence
	margins, at(candidate_parents=(0 1 2) candidate_policy=(0)) post coefleg
	eststo com_parent_intpolicy0
		coefplot, 	$marker_ci $coeflabels_parents $mean_com  $notbot $NOPolicy
					graph save  int_policy_parent_competence0.gph, replace
	estimates restore model_int_policy_competence
	margins, at(candidate_parents=(0 1 2) candidate_policy=(1)) post
	eststo com_parent_intpolicy1
		coefplot,	$marker_ci $coeflabels_parents $mean_com  $notbot $LPolicy
				graph save  int_policy_parent_competence1.gph, replace
	estimates restore model_int_policy_competence
	margins, at(candidate_parents=(0 1 2) candidate_policy=(2)) post
	eststo com_parent_intpolicy2
			coefplot, 	$marker_ci $coeflabels_parents $mean_com $RPolicy $bottom  
				graph save  int_policy_parent_competence2.gph, replace
			
	graph combine 		int_policy_parent_competence0.gph int_policy_parent_competence1.gph  int_policy_parent_competence2.gph, imargin(small) iscale(1) title("              Competence") xcommon col(1) fxsize(110)
	graph save com_int_pol_parent.gph, replace		
				
		
	*Warmth
	estimates restore model_int_policy_warmth
	margins, at(candidate_parents=(0 1 2) candidate_policy=(0)) post coefleg
	eststo com_parent_intpolicy0
		coefplot, 	$marker_ci $coeflabels_parents $mean_war  $notbot $NOPolicy
					graph save  int_policy_parent_warmth0.gph, replace
	estimates restore model_int_policy_warmth
	margins, at(candidate_parents=(0 1 2) candidate_policy=(1)) post
	eststo com_parent_intpolicy1
		coefplot,	$marker_ci $coeflabels_parents $mean_war  $notbot $LPolicy
				graph save  int_policy_parent_warmth1.gph, replace
	estimates restore model_int_policy_warmth
	margins, at(candidate_parents=(0 1 2) candidate_policy=(2)) post
	eststo com_parent_intpolicy2
			coefplot, 	$marker_ci $coeflabels_parents $mean_war $bottom $RPolicy   
				graph save  int_policy_parent_warmth2.gph, replace
			
	graph combine 		int_policy_parent_warmth0.gph int_policy_parent_warmth1.gph  int_policy_parent_warmth2.gph, imargin(small) title("                        Warmth") iscale(1) xcommon col(1) fxsize(100)
						graph save war_int_pol_parent.gph, replace		
	
	
	*LR
	estimates restore model_int_policy_LR
	margins, at(candidate_parents=(0 1 2) candidate_policy=(0)) post coefleg
	eststo com_parent_intpolicy0
		coefplot, 	$marker_ci $coeflabels_parents $mean_LR  $notbot $notleft $NOPolicy
					graph save  int_policy_parent_LR0.gph, replace
	estimates restore model_int_policy_LR
	margins, at(candidate_parents=(0 1 2) candidate_policy=(1)) post
	eststo com_parent_intpolicy1
		coefplot,	$marker_ci $coeflabels_parents $mean_LR  $notbot $notleft $LPolicy
					graph save  int_policy_parent_LR1.gph, replace
	estimates restore model_int_policy_LR
	margins, at(candidate_parents=(0 1 2) candidate_policy=(2)) post
	eststo com_parent_intpolicy2
		coefplot, 	$marker_ci $coeflabels_parents $mean_LR $notleft $bottom $RPolicy   
					graph save  int_policy_parent_LR2.gph, replace
			
	graph combine 		int_policy_parent_LR0.gph int_policy_parent_LR1.gph  int_policy_parent_LR2.gph, imargin(small) title("Left-Right") iscale(1) xcommon col(1) fxsize(70)
						graph save LR_int_pol_parent.gph, replace		
	
	
					
	*Vote
	estimates restore model_int_policy_vote
	margins, at(candidate_parents=(0 1 2) candidate_policy=(0)) post coefleg
	eststo com_parent_intvote0
		coefplot, 	$marker_ci $coeflabels_parents $mean_vote  $notbot $notleft $NOPolicy
					graph save  int_vote_parent_vote0.gph, replace
	estimates restore model_int_policy_vote
	margins, at(candidate_parents=(0 1 2) candidate_policy=(1)) post
	eststo com_parent_intvote1
		coefplot,	$marker_ci $coeflabels_parents $mean_vote  $notbot $notleft $LPolicy
					graph save  int_policy_parent_vote1.gph, replace
	estimates restore model_int_policy_vote
	margins, at(candidate_parents=(0 1 2) candidate_policy=(2)) post
	eststo com_parent_intvote2
		coefplot, 	$marker_ci $coeflabels_parents $mean_vote $notleft $bottom $RPolicy   
					graph save  int_policy_parent_vote2.gph, replace
			
	graph combine 		int_vote_parent_vote0.gph int_policy_parent_vote1.gph  int_policy_parent_vote2.gph, imargin(small) title("Vote") xcommon iscale(1) col(1) fxsize(70)
						graph save vote_int_pol_parent.gph, replace		
	
		
	*NEW: FIGURE 3*FIGURE 3: Voter Evaluations of a Candidate, Conditional on Occupation of Candidate's Parents and Policy Information
	graph combine 	war_int_pol_parent.gph LR_int_pol_parent.gph vote_int_pol_parent.gph , imargin(zero) ysize(3.5) xsize(6.5) col(4) iscale(1) ///
					title("", size(med) margin(bottom)) ///
					note("Note: Predicted values with 95% Confidence intervals (Thicker lines are 83% C.I.)." "Vertical dashed lines denote the mean value of variable.", size(medsmall))
					graph export FIGURE_3NEW.emf, replace
			
	
	
*Own occupation
	*Competence 
	estimates restore model_int_policy_competence
	margins, at(candidate_job=(0 1 2) candidate_policy=(0)  ) post coefleg
	eststo com_job_intpolicy0
		coefplot, 	$marker_ci $coeflabels_job $mean_com  $notbot $NOPolicy
					graph save  int_policy_job_competence0.gph, replace
	estimates restore model_int_policy_competence
	margins, at(candidate_job=(0 1 2) candidate_policy=(1)  ) post
	eststo com_job_intpolicy1
		coefplot,	$marker_ci $coeflabels_job $mean_com  $notbot $LPolicy
				graph save  int_policy_job_competence1.gph, replace
	estimates restore model_int_policy_competence
	margins, at(candidate_job=(0 1 2) candidate_policy=(2)  ) post
	eststo com_job_intpolicy2
			coefplot, 	$marker_ci $coeflabels_job $mean_com $RPolicy $bottom  
				graph save  int_policy_job_competence2.gph, replace
			
	graph combine 		int_policy_job_competence0.gph int_policy_job_competence1.gph  int_policy_job_competence2.gph, imargin(small) iscale(1) title("                            Competence") xcommon col(1) fxsize(110)
	graph save com_int_pol_job.gph, replace		
		
			
		
	*Warmth
	estimates restore model_int_policy_warmth
	margins, at(candidate_job=(0 1 2) candidate_policy=(0)  ) post coefleg
	eststo com_job_intpolicy0
		coefplot, 	$marker_ci $coeflabels_job $mean_war  $notbot $notleft $NOPolicy
					graph save  int_policy_job_warmth0.gph, replace
	estimates restore model_int_policy_warmth
	margins, at(candidate_job=(0 1 2) candidate_policy=(1)  ) post
	eststo com_job_intpolicy1
		coefplot,	$marker_ci $coeflabels_job $mean_war  $notbot $notleft $LPolicy
				graph save  int_policy_job_warmth1.gph, replace
	estimates restore model_int_policy_warmth
	margins, at(candidate_job=(0 1 2) candidate_policy=(2)  ) post
	eststo com_job_intpolicy2
			coefplot, 	$marker_ci $coeflabels_job $mean_war $notleft $bottom $RPolicy   
				graph save  int_policy_job_warmth2.gph, replace
			
	graph combine 		int_policy_job_warmth0.gph int_policy_job_warmth1.gph  int_policy_job_warmth2.gph, imargin(small) iscale(1)  title("Warmth") xcommon col(1) fxsize(70)
						graph save war_int_pol_job.gph, replace		
	
	
	*LR
	estimates restore model_int_policy_LR
	margins, at(candidate_job=(0 1 2) candidate_policy=(0)  ) post coefleg
	eststo com_job_intpolicy0
		coefplot, 	$marker_ci $coeflabels_job $mean_LR  $notbot $notleft $NOPolicy
					graph save  int_policy_job_LR0.gph, replace
	estimates restore model_int_policy_LR
	margins, at(candidate_job=(0 1 2) candidate_policy=(1) ) post
	eststo com_job_intpolicy1
		coefplot,	$marker_ci $coeflabels_job $mean_LR  $notbot $notleft $LPolicy
					graph save  int_policy_job_LR1.gph, replace
	estimates restore model_int_policy_LR
	margins, at(candidate_job=(0 1 2) candidate_policy=(2)  ) post
	eststo com_job_intpolicy2
		coefplot, 	$marker_ci $coeflabels_job $mean_LR $notleft $bottom $RPolicy   
					graph save  int_policy_job_LR2.gph, replace
			
	graph combine 		int_policy_job_LR0.gph int_policy_job_LR1.gph  int_policy_job_LR2.gph, imargin(small) iscale(1) title("Left-Right") xcommon col(1) fxsize(70)
						graph save LR_int_pol_job.gph, replace		
	
	
					
	*Vote
	estimates restore model_int_policy_vote
	margins, at(candidate_job=(0 1 2) candidate_policy=(0)  ) post coefleg
	eststo com_job_intvote0
		coefplot, 	$marker_ci $coeflabels_job $mean_vote  $notbot $notleft $NOPolicy
					graph save  int_vote_job_vote0.gph, replace
	estimates restore model_int_policy_vote
	margins, at(candidate_job=(0 1 2) candidate_policy=(1)  ) post
	eststo com_job_intvote1
		coefplot,	$marker_ci $coeflabels_job $mean_vote  $notbot $notleft $LPolicy
					graph save  int_policy_job_vote1.gph, replace
	estimates restore model_int_policy_vote
	margins, at(candidate_job=(0 1 2) candidate_policy=(2)  ) post
	eststo com_job_intvote2
		coefplot, 	$marker_ci $coeflabels_job $mean_vote $notleft $bottom $RPolicy   
					graph save  int_policy_job_vote2.gph, replace
			
	graph combine 		int_vote_job_vote0.gph int_policy_job_vote1.gph  int_policy_job_vote2.gph, imargin(small) iscale(1)  title("Vote") xcommon col(1) fxsize(70)
						graph save vote_int_pol_job.gph, replace		
	
	*NEW: FIGURE 4*FIGURE 4: Judgement of Candidate, Conditional on Own Occupation and Policy information
	graph combine 	com_int_pol_job.gph war_int_pol_job.gph LR_int_pol_job.gph vote_int_pol_job.gph , imargin(zero) ysize(3.5) xsize(6.5) col(4) iscale(1) ///
					title("", size(medlarge) margin(bottom)) ///
					note("Note: Predicted values with 95% Confidence intervals (Thicker lines are 83% C.I.)." "Vertical dashed lines denote the mean value of variable.", size(medsmall))
					graph export FIGURE_4NEW.emf, replace			
	
	
*Figures for appendix C*
		*Left-leaning respondent
		sum LR, de
		 //a 90 percentile respondent has a LR placement at approx. 0.9
		
			*Competence 
			estimates restore model_int_policy_comp_LR
			margins, at(candidate_job=(0 1 2) candidate_policy=(0) LR=.1 ) post coefleg
			eststo com_job_intpolicy0_LR
				coefplot, 	$marker_ci $coeflabels_job $mean_com  $notbot $NOPolicy
							graph save  int_policy_job_comp0LR.gph, replace
			estimates restore model_int_policy_comp_LR
			margins, at(candidate_job=(0 1 2) candidate_policy=(1) LR=.1 ) post
			eststo com_job_intpolicy1_LR
				coefplot,	$marker_ci $coeflabels_job $mean_com  $notbot $LPolicy
						graph save  int_policy_job_comp1LR.gph, replace
			estimates restore model_int_policy_comp_LR
			margins, at(candidate_job=(0 1 2) candidate_policy=(2) LR=.1 ) post
			eststo com_job_intpolicy2_LR
					coefplot, 	$marker_ci $coeflabels_job $mean_com $RPolicy $bottom  
						graph save  int_policy_job_comp2LR.gph, replace
					
			graph combine 		int_policy_job_comp0LR.gph int_policy_job_comp1LR.gph  int_policy_job_comp2LR.gph, imargin(small) iscale(1) title("                            Competence") xcommon col(1) fxsize(110)
			graph save com_int_pol_jobLR.gph, replace		
				
					
				
			*Warmth
			estimates restore model_int_policy_warmth_LR
			margins, at(candidate_job=(0 1 2) candidate_policy=(0) LR=.1 ) post coefleg
			eststo com_job_intpolicy0_LR
				coefplot, 	$marker_ci $coeflabels_job $mean_war  $notbot $notleft $NOPolicy
							graph save  int_policy_job_warmth0LR.gph, replace
			estimates restore model_int_policy_warmth_LR
			margins, at(candidate_job=(0 1 2) candidate_policy=(1) LR=.1 ) post
			eststo com_job_intpolicy1_LR
				coefplot,	$marker_ci $coeflabels_job $mean_war  $notbot $notleft $LPolicy
						graph save  int_policy_job_warmth1LR.gph, replace
			estimates restore model_int_policy_warmth_LR
			margins, at(candidate_job=(0 1 2) candidate_policy=(2) LR=.1 ) post
			eststo com_job_intpolicy2_LR
					coefplot, 	$marker_ci $coeflabels_job $mean_war $notleft $bottom $RPolicy   
						graph save  int_policy_job_warmth2LR.gph, replace
					
			graph combine 		int_policy_job_warmth0LR.gph int_policy_job_warmth1LR.gph  int_policy_job_warmth2LR.gph, imargin(small) iscale(1)  title("Warmth") xcommon col(1) fxsize(70)
								graph save war_int_pol_jobLR.gph, replace		
			
			
			*LR
			estimates restore model_int_policy_LR_LR
			margins, at(candidate_job=(0 1 2) candidate_policy=(0) LR=.1  ) post coefleg
			eststo com_job_intpolicy0LR
				coefplot, 	$marker_ci $coeflabels_job $mean_LR  $notbot $notleft $NOPolicy
							graph save  int_policy_job_LR0LR.gph, replace
			estimates restore model_int_policy_LR_LR
			margins, at(candidate_job=(0 1 2) candidate_policy=(1) LR=.1 ) post
			eststo com_job_intpolicy1LR
				coefplot,	$marker_ci $coeflabels_job $mean_LR  $notbot $notleft $LPolicy
							graph save  int_policy_job_LR1LR.gph, replace
			estimates restore model_int_policy_LR_LR
			margins, at(candidate_job=(0 1 2) candidate_policy=(2) LR=.1 ) post
			eststo com_job_intpolicy2LR
				coefplot, 	$marker_ci $coeflabels_job $mean_LR $notleft $bottom $RPolicy   
							graph save  int_policy_job_LR2LR.gph, replace
					
			graph combine 		int_policy_job_LR0LR.gph int_policy_job_LR1LR.gph  int_policy_job_LR2LR.gph, imargin(small) iscale(1) title("Left-Right") xcommon col(1) fxsize(70)
								graph save LR_int_pol_jobLR.gph, replace		
			
			
							
			*Vote
			estimates restore model_int_policy_vote_LR
			margins, at(candidate_job=(0 1 2) candidate_policy=(0) LR=.1  ) post coefleg
			eststo com_job_intvote0LR
				coefplot, 	$marker_ci $coeflabels_job $mean_vote  $notbot $notleft $NOPolicy
							graph save  int_vote_job_vote0LR.gph, replace
			estimates restore model_int_policy_vote_LR
			margins, at(candidate_job=(0 1 2) candidate_policy=(1) LR=.1  ) post
			eststo com_job_intvote1LR
				coefplot,	$marker_ci $coeflabels_job $mean_vote  $notbot $notleft $LPolicy
							graph save  int_policy_job_vote1LR.gph, replace
			estimates restore model_int_policy_vote_LR
			margins, at(candidate_job=(0 1 2) candidate_policy=(2) LR=.1  ) post
			eststo com_job_intvote2LR
				coefplot, 	$marker_ci $coeflabels_job $mean_vote $notleft $bottom $RPolicy   
							graph save  int_policy_job_vote2LR.gph, replace
					
			graph combine 		int_vote_job_vote0LR.gph int_policy_job_vote1LR.gph  int_policy_job_vote2LR.gph, imargin(small) iscale(1)  title("Vote") xcommon col(1) fxsize(70)
								graph save vote_int_pol_jobLR.gph, replace		
			
			*Appendix C figure*FIGURE C1: Left-Leaning Respondent's Judgement of Candidate, Conditional on Occupation and Policy information
			graph combine 	com_int_pol_jobLR.gph war_int_pol_jobLR.gph LR_int_pol_jobLR.gph vote_int_pol_jobLR.gph , imargin(zero) ysize(3.5) xsize(6.5) col(4) iscale(1) ///
							title("", size(medium) margin(bottom)) ///
							note("Note: Predicted values with 95% Confidence intervals (Thicker lines are 83% C.I.)." "Vertical dashed lines denote the mean value of variable." ///
							"Left-leaning respondent defined as respondent with LR-self placement at the 10th percentile (LR=.1)", size(medsmall))
							graph export FIGURE_C1.emf, replace

		*Right-leaning respondent
		sum LR, de
		 //a 90 percentile respondent has a LR placement at approx. 0.9
		
			*Competence 
			estimates restore model_int_policy_comp_LR
			margins, at(candidate_job=(0 1 2) candidate_policy=(0) LR=.9 ) post coefleg
			eststo com_job_intpolicy0_LR
				coefplot, 	$marker_ci $coeflabels_job $mean_com  $notbot $NOPolicy
							graph save  int_policy_job_comp0LR.gph, replace
			estimates restore model_int_policy_comp_LR
			margins, at(candidate_job=(0 1 2) candidate_policy=(1) LR=.9 ) post
			eststo com_job_intpolicy1_LR
				coefplot,	$marker_ci $coeflabels_job $mean_com  $notbot $LPolicy
						graph save  int_policy_job_comp1LR.gph, replace
			estimates restore model_int_policy_comp_LR
			margins, at(candidate_job=(0 1 2) candidate_policy=(2) LR=.9 ) post
			eststo com_job_intpolicy2_LR
					coefplot, 	$marker_ci $coeflabels_job $mean_com $RPolicy $bottom  
						graph save  int_policy_job_comp2LR.gph, replace
					
			graph combine 		int_policy_job_comp0LR.gph int_policy_job_comp1LR.gph  int_policy_job_comp2LR.gph, imargin(small) iscale(1) title("                            Competence") xcommon col(1) fxsize(110)
			graph save com_int_pol_jobLR.gph, replace		
				
					
				
			*Warmth
			estimates restore model_int_policy_warmth_LR
			margins, at(candidate_job=(0 1 2) candidate_policy=(0) LR=.9 ) post coefleg
			eststo com_job_intpolicy0_LR
				coefplot, 	$marker_ci $coeflabels_job $mean_war  $notbot $notleft $NOPolicy
							graph save  int_policy_job_warmth0LR.gph, replace
			estimates restore model_int_policy_warmth_LR
			margins, at(candidate_job=(0 1 2) candidate_policy=(1) LR=.9 ) post
			eststo com_job_intpolicy1_LR
				coefplot,	$marker_ci $coeflabels_job $mean_war  $notbot $notleft $LPolicy
						graph save  int_policy_job_warmth1LR.gph, replace
			estimates restore model_int_policy_warmth_LR
			margins, at(candidate_job=(0 1 2) candidate_policy=(2) LR=.9 ) post
			eststo com_job_intpolicy2_LR
					coefplot, 	$marker_ci $coeflabels_job $mean_war $notleft $bottom $RPolicy   
						graph save  int_policy_job_warmth2LR.gph, replace
					
			graph combine 		int_policy_job_warmth0LR.gph int_policy_job_warmth1LR.gph  int_policy_job_warmth2LR.gph, imargin(small) iscale(1)  title("Warmth") xcommon col(1) fxsize(70)
								graph save war_int_pol_jobLR.gph, replace		
			
			
			*LR
			estimates restore model_int_policy_LR_LR
			margins, at(candidate_job=(0 1 2) candidate_policy=(0) LR=.9  ) post coefleg
			eststo com_job_intpolicy0LR
				coefplot, 	$marker_ci $coeflabels_job $mean_LR  $notbot $notleft $NOPolicy
							graph save  int_policy_job_LR0LR.gph, replace
			estimates restore model_int_policy_LR_LR
			margins, at(candidate_job=(0 1 2) candidate_policy=(1) LR=.9 ) post
			eststo com_job_intpolicy1LR
				coefplot,	$marker_ci $coeflabels_job $mean_LR  $notbot $notleft $LPolicy
							graph save  int_policy_job_LR1LR.gph, replace
			estimates restore model_int_policy_LR_LR
			margins, at(candidate_job=(0 1 2) candidate_policy=(2) LR=.9 ) post
			eststo com_job_intpolicy2LR
				coefplot, 	$marker_ci $coeflabels_job $mean_LR $notleft $bottom $RPolicy   
							graph save  int_policy_job_LR2LR.gph, replace
					
			graph combine 		int_policy_job_LR0LR.gph int_policy_job_LR1LR.gph  int_policy_job_LR2LR.gph, imargin(small) iscale(1) title("Left-Right") xcommon col(1) fxsize(70)
								graph save LR_int_pol_jobLR.gph, replace		
			
			
							
			*Vote
			estimates restore model_int_policy_vote_LR
			margins, at(candidate_job=(0 1 2) candidate_policy=(0) LR=.9 ) post coefleg
			eststo com_job_intvote0LR
				coefplot, 	$marker_ci $coeflabels_job $mean_vote  $notbot $notleft $NOPolicy
							graph save  int_vote_job_vote0LR.gph, replace
			estimates restore model_int_policy_vote_LR
			margins, at(candidate_job=(0 1 2) candidate_policy=(1) LR=.9 ) post
			eststo com_job_intvote1LR
				coefplot,	$marker_ci $coeflabels_job $mean_vote  $notbot $notleft $LPolicy
							graph save  int_policy_job_vote1LR.gph, replace
			estimates restore model_int_policy_vote_LR
			margins, at(candidate_job=(0 1 2) candidate_policy=(2) LR=.9 ) post
			eststo com_job_intvote2LR
				coefplot, 	$marker_ci $coeflabels_job $mean_vote $notleft $bottom $RPolicy   
							graph save  int_policy_job_vote2LR.gph, replace
					
			graph combine 		int_vote_job_vote0LR.gph int_policy_job_vote1LR.gph  int_policy_job_vote2LR.gph, imargin(small) iscale(1)  title("Vote") xcommon col(1) fxsize(70)
								graph save vote_int_pol_jobLR.gph, replace		
			
			*Appendix C figure*FIGURE C2: Right-Leaning Respondent's Judgement of Candidate, Conditional on Occupation and Policy information
			graph combine 	com_int_pol_jobLR.gph war_int_pol_jobLR.gph LR_int_pol_jobLR.gph vote_int_pol_jobLR.gph , imargin(zero) ysize(3.5) xsize(6.5) col(4) iscale(1) ///
							title("", size(medium) margin(bottom)) ///
							note("Note: Predicted values with 95% Confidence intervals (Thicker lines are 83% C.I.)." "Vertical dashed lines denote the mean value of variable." ///
							"Right-leaning respondent defined as respondent with LR-self placement at the 90th percentile (LR=.8)", size(medsmall))
							graph export FIGURE_C2.emf, replace
	
	
************
***TABLES***
************


*Models used in paper
esttab 	model_int_policy_warmth model_int_policy_competence model_int_policy_LR model_int_policy_vote using interactions_right.rtf, label r2 b(3) se(3) nobase nogaps  replace ///
		refcat(1.candidate_female "{\i Gender:}" 1.candidate_parents "{\i Parent's occupation:}" 1.candidate_job "{\i Candidate's occupation:}" 1.candidate_policy "{\i Policy information:}" _cons "   ",  nolabel) 		///
		order(1.candidate_female 1.candidate_female#1.candidate_policy 1.candidate_female#2.candidate_policy 1.candidate_parents 2.candidate_parents ///
		1.candidate_parents#1.candidate_policy 1.candidate_parents#2.candidate_policy 2.candidate_parents#1.candidate_policy 2.candidate_parents#2.candidate_policy ///
		1.candidate_job 2.candidate_job 1.candidate_job#1.candidate_policy 1.candidate_job#2.candidate_policy 2.candidate_job#1.candidate_policy 2.candidate_job#2.candidate_policy ///
		1.candidate_policy 2.candidate_policy)

*Models without interactions		
esttab 	model_warmth model_competence model_LR model_vote using models.rtf , label r2 b(3) se(3) nobase nogaps replace ///
		refcat(1.candidate_female "{\i Gender:}" 1.candidate_parents "{\i Parent's occupation:}" 1.candidate_job "{\i Candidate's occupation:}" 1.candidate_policy "{\i Policy information:}" _cons "   ",  nolabel) 

*Models with respondent covariates
esttab 	model_warmth_contr model_competence_contr model_LR_contr model_vote_contr using models_contr.rtf , label r2 b(3) se(3) nobase nogaps replace ///
		refcat(1.candidate_female "{\i Gender:}" 1.candidate_parents "{\i Parent's occupation:}" 1.candidate_job "{\i Candidate's occupation:}" 1.candidate_policy "{\i Policy information:}" ///
		1.female "{\i Respondent Characteristics:}" _cons "   ",  nolabel)
				
*Models with interactions between parents' occupation and candidate's occupation
esttab 	model_warmth_par_job model_competence_par_job model_LR_par_job model_vote_par_job using models_par_job.rtf , label r2 b(3) se(3) nobase nogaps replace ///
		refcat(1.candidate_female "{\i Gender:}" 1.candidate_parents "{\i Parent's occupation:}" 1.candidate_job "{\i Candidate's occupation:}" ///
		1.candidate_parents#1.candidate_job "{\i Parent # Candidate Occupation:}"  1.candidate_policy "{\i Policy information:}" _cons "   ",  nolabel) 		

*Models with interactions between treatments and respondents' Left-Right position: NOT INCLUDED IN PAPER - IT IS WAY TOO BIG
esttab 	model_int_policy_warmth_LR model_int_policy_comp_LR model_int_policy_LR_LR model_int_policy_vote_LR using interactions_right_LR2.rtf , label r2 b(3) se(3) nobase nogaps  replace ///
		refcat(1.candidate_female "{\i Gender:}" 1.candidate_parents "{\i Parent's occupation:}" 1.candidate_job "{\i Candidate's occupation:}" 1.candidate_policy "{\i Policy information:}" _cons "   ",  nolabel) 		///
		order(1.candidate_female 1.candidate_female#1.candidate_policy 1.candidate_female#2.candidate_policy 1.candidate_parents 2.candidate_parents ///
		1.candidate_parents#1.candidate_policy 1.candidate_parents#2.candidate_policy 2.candidate_parents#1.candidate_policy 2.candidate_parents#2.candidate_policy ///
		1.candidate_job 2.candidate_job 1.candidate_job#1.candidate_policy 1.candidate_job#2.candidate_policy 2.candidate_job#1.candidate_policy 2.candidate_job#2.candidate_policy ///
		1.candidate_policy 2.candidate_policy)


**************************
*Erasing temporary graphs*
**************************
cd "C:\Voter_evaluations"
!del *gph //make sure you are in the right directory when running this line

***********************************
***THE END*************************
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